Get a hands-on experience of using ML / DL for solving computer vision problems

4 Nov 2017, 9:30 AM to 6:00 PM, Innov8 Coworking, Bengaluru

The human body is one of the most complex machines on Earth. We are fascinated by how the Human Visual System works. How as a human, we see the world, store the visual information and learn from what we see and recognize patterns from previous experiences. The goal of the workshop is to help build an understanding of how to solve real world problems using Computer Vision with examples. We start from biological motivations for Computer Vision, developing intuitions to solve problems, converting the intuitions into the language of mathematics and finally developing code that represents the mathematics. With the help of Machine Learning and Deep learning, we are able to attain state-of-art performance in many Computer Vision problems. The workshop is meant for those who want to get get a hands-on experience of using ML / DL for solving Computer Vision problems.

Who is it for?

Beginners in any of Computer Vision, Machine Learning and Deep learning

Or those with exposure to ML / DL and new to ML / DL in Computer Vision

Prerequisites

Background Knowledge

Good Experience in Python (can not support non programmers during session due to lack of time)

Good to have: Knowledge of Numpy

Devices

PC with minimum configuration: 8 GB RAM and i5 Processor

Install VirtualBox Software & Download and run image provided

Software

The participants have to install the required software before the session (link will be provided shortly).

We will conduct a Installation Clinic to help the participants install the software package one day before the session.

We would like to have one volunteer to help the participants in case of any software conflicts

Outline

The workshop builds an intuition behind how a digital image is captured, stored and processed. It aims to show what are the traditional and simple object detection mechanisms in Computer Vision and their limitations by examples. Then we show how Machine Learning came to the aid and solved the problems which the traditional CV techniques could not solve.

We will spend time on analyzing the limitations of Machine Learning and how we can address some of these using the Deep Learning techniques. We will dive into the Black box (DL) and try to understand what each layer is doing and so that we can solve problems in an effective manner. We will finally talk about best practises in solving Computer Vision problems, which technique to use, which parameter to tweak, etc.,

The workshop is going to have 3 major parts each with a example problems that we will experiment on, using Jupyter notebooks. At the end of the workshop, each participant should be able to build a network using Keras (Python library for Deep Learning), train and test the model. It is going to be a hands-on and with some mathematics, especially suitable for the beginners to Computer Vision or practitioners who have not had a chance to build from basics.

Part I

Motivation: Interesting applications of Computer Vision

What is Computer Vision, Machine Vision and Image Processing ?

Simple Computer Vision based classification (hands-on)

Part II

Machine Learning in CV

Classification using ML (hands-on)

Part III

Emergence and Dominance of Deep Learning

Applications of DL (hands-on)

Compare ML and DL (hands-on)

How to solve a CV problem by choosing the appropriate technique?

Instructors

Sumod Mohan

CTO of Digital Aristotle and heads the Computer Vision and Machine Learning at Soliton Technologies

Sumod Mohan

CTO of Digital Aristotle and heads the Computer Vision and Machine Learning at Soliton Technologies

Sumod Mohan is CTO of Digital Aristotle and heads the Computer Vision and Machine Learning at Soliton Technologies. His experience spans Computer Vision, Machine Learning, 3D Vision, Deep Learning, NLP, Graph Algorithms, Probabilistic Graphical Models, Code Optimization and Parallelization and has worked in the Computer Vision and Machine Learning for past 10+ years. His broad research interest is in application of Graph Algorithms and Probabilistic Graphical Models in Computer Vision and holds an M.S degree from Clemson University, USA with specialization in Intelligent Systems and Robotics. Prior to this after dropping out of his Ph.D program, he worked for HighlightCam Inc, a startup in California where he led Computer Vision Algorithm Development.

Shivarajkumar Magadi

Leads the 3D Vision team at Soliton Technologies

Shivarajkumar Magadi

Leads the 3D Vision team at Soliton Technologies

Shivaraj is currently leading 3D Vision team in Soliton Technologies and his prior experience includes 3D pose estimation, 3D depth estimation, segmentation, pattern recognition and machine learning. The products developed include Monocular Augmented Reality Application, Classification of manufactured components, Pose Estimation for large nearly-rigid objects etc. He has more than 4+ years experience developing and deploying products in 3D Vision and Machine Vision.

Dhivakar Kanagaraj

Computer Vision and Machine Learning Engineer at Soliton Technologies

Dhivakar Kanagaraj

Computer Vision and Machine Learning Engineer at Soliton Technologies

Dhivakar is currently working as a Computer Vision and Machine Learning Engineer at Soliton Technologies. He has been working for the past 2+ years on Object detection and Recognition problems with Computer Vision and Deep Learning. He has also been a co-organizer and in-charge of the Bangalore Computer Vision Meetup (BCVM): a forum for discussing research papers on Computer Vision, Machine Learning and Deep Learning.

Senthil Palanisamy

Computer Vision and Machine Learning Engineer in Soliton Technologies

Senthil Palanisamy

Computer Vision and Machine Learning Engineer in Soliton Technologies

Senthil is currently working as a Computer Vision and Machine Learning Engineer in Soliton Technologies. His research interest lies in the intersection of Deep Learning and Graph Algorithms. He completed his bachelor’s degree in Electronics and Communication Engineering in Coimbatore Institute of Technology.

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